ML promises to transform robotics: teach robots through demonstrations instead of code.
The field is early. The ecosystem lacks dedicated tools to make development simple, repeatable, and accessible:
- Data collection is expensive: hardware integration, teleoperation setup, dataset curation all require specialized expertise
- Data is messy: multi-rate sensors, format fragmentation, re-recording for each framework
- Deployment is complex: vendor-specific APIs, hardware compatibility issues, monitoring infrastructure from scratch
Teams spend more time fighting infrastructure than building capabilities.
Positronic is an end-to-end toolkit for ML-driven robotics.
It covers the full lifecycle: bring hardware online, capture and curate datasets, train and evaluate policies, deploy inference, and iterate.
Connect any hardware to any AI model. Store your data once, train on any framework. Deploy with a unified inference API. All in plain Python, no ROS required.
How Positronic differs from LeRobot
LeRobot focuses on training: fast experiments on reference hardware and public datasets. Positronic adds the operational infrastructure: hardware drivers, data collection tools, unified inference API, and iteration workflows. We use LeRobot for training. Positronic adds the lifecycle management around it.
Get involved
- 💬 Join Discord to ask questions, share setups, and request features.
- ⭐ Star on GitHub to follow our progress.
- ✉️ Email: [email protected]